Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data

Towards Understanding and Characterizing the Arbitrage Bot Scam In the Wild

This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To collect the scam at a large scale, we developed a fully automated scam detection system named CryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile, CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deploying CryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD. Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.Comment: Accepted by ACM SIGMETRICS 202

Association Rules Mining among Interests and Applications for Users on Social Networks

Interest is an important concept in psychology and pedagogy and is widely studied in many fields. Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks. However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data. In this paper, we propose and verify two hypotheses about the interests of social network users. We then use association rules to mine users' interests from LinkedIn users' profiles. Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness. According to our research, we found that there are a large number of association rules between human interests. These rules play a considerable role in our method of interest mining. Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value. The research work has certain theoretical and practical value

Google Trends as a complementary tool for new car sales forecasting : a cross-country comparison along the customer journey, a case for India and South Korea : Master of Business Studies in Marketing at Massey University, Albany, New Zealand

Figures 5 & 6 © 2021 Bennett, Coleman & Co. Ltd were deleted for copyright reasons, but may be accessed via the source listed in the References.This dissertation aims to evaluate factors responsible for different consumption patterns and customer's preferences for goods and services in India and South Korea. It further analyzes several model frameworks that reflect the different variants of conceptual ideas of different authors. It gives special importance to customer-oriented study with the aim of improving customer-oriented analysis. It also discusses the assessment of economic conditions of India and South Korea as purchasing power of customers is directly proportional to the state of the economy. It further discusses challenges involved in pricing of new car models. The focus is given to quantitative research to evaluate the correlation between the customer preference for a particular model of car and data of new car sales. This purpose is achieved using several linear regression models and cross-sectional studies. It has been observed that seasonality has little impact on car prices in India as per the data retrieved from Google Trend Index but in South Korea seasonality has significant impact on car prices. This dissertation also discusses the significance of Google Trend Index in both the countries. It further provides that use of studies used in this dissertation can serve as a valuable tool for predicting the demand for various car models in both the countries which would not be accurately provided by use of traditional approaches

A personality-based behavioural model: Susceptibility to phishing on social networking sites

The worldwide popularity of social networking sites (SNSs) and the technical features they offer users have created many opportunities for malicious individuals to exploit the behavioral tendencies of their users via social engineering tactics. The self-representation and social interactions on SNSs encourage users to reveal their personalities in a way which characterises their behaviour. Frequent engagement on SNSs may also reinforce the performance of certain activities, such as sharing and clicking on links, at a “habitual” level on these sites. Subsequently, this may also influence users to overlook phishing posts and messages on SNSs and thus not apply sufficient cognitive effort in their decision-making. As users do not expect phishing threats on these sites, they may become accustomed to behaving in this manner which may consequently put them at risk of such attacks. Using an online survey, primary data was collected from 215 final-year undergraduate students. Employing structural equation modelling techniques, the associations between the Big Five personality traits, habits and information processing were examined with the aim to identify users susceptible to phishing on SNSs. Moreover, other behavioural factors such as social norms, computer self-efficacy and perceived risk were examined in terms of their influence on phishing susceptibility. The results of the analysis revealed the following key findings: 1) users with the personality traits of extraversion, agreeableness and neuroticism are more likely to perform habitual behaviour, while conscientious users are least likely; 2) users who perform certain behaviours out of habit are directly susceptible to phishing attacks; 3) users who behave out of habit are likely to apply a heuristic mode of processing and are therefore more susceptible to phishing attacks on SNSs than those who apply systematic processing; 4) users with higher computer self-efficacy are less susceptible to phishing; and 5) users who are influenced by social norms are at greater risk of phishing. This study makes a contribution to scholarship and to practice, as it is the first empirical study to investigate, in one comprehensive model, the relationship between personality traits, habit and their effect on information processing which may influence susceptibility to phishing on SNSs. The findings of this study may assist organisations in the customisation of an individual anti-phishing training programme to target specific dispositional factors in vulnerable users. By using a similar instrument to the one used in this study, pre-assessments could determine and classify certain risk profiles that make users vulnerable to phishing attacks.Thesis (PhD) -- Faculty of Commerce, Information Systems, 202

Investigating the deceptive information in twitter spam

Online Social Networks (OSNs) such as Facebook and Twitter have become popular communication and information sharing tools for hundreds of millions of individuals in recent years. OSNs not only make people’s life more connected, but also attract the interest of spammers. Twitter spam generally contains deceptive information, such as “free voucher” and “weight loss advertisement” to attract the interest of victims. A comprehensive analysis on the deceptive information will be of great benefit to the detection of Twitter spam. This paper presents a study of deceptive information in Twitter spam. The analysis is based on a collection of over 550 million tweets with around 6% spam. We find that various deceptive content of spam performs differently in luring victims to malicious sites. We also find the regional response rate to various Twitter spam outbreaks varies greatly. These two factors can contribute to improve the performance of spam detection techniques